An autonomous car (also known as a driverless car, self-driving car, or robotic car) is a vehicle that navigates without human control. An autonomous vehicle senses its environment to detect surroundings using radar, Lidar, GPS, Odometer, or computer vision. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage. Autonomous cars are equipped with control systems for analyzing sensory data in order to distinguish between different cars or obstacles on the road. Currently, driverless technology is developed by Google®, Tesla®, and some other vehicles manufactures, such as Audi®, BMW®, Nissan®, and the like.
Other companies such as, e.g., Mobileye®, are in the marketplace trying to provide solutions for hands-free driving technology. Use of this technology is typically limited to particular driving infrastructures such as, e.g., highways or country roads. The corner-stone of such hands-free driving and autonomous vehicles technologies is the rendering or generation of a 3-dimensional (3D) map of a scene at any given moment during or immediately prior to motion. Such a map tries to mimic a scene as would have been seen by a driver.
In addition, other technologies utilize 3D maps for, e.g., navigational purposes. Drones and other vehicles may utilize 3D maps at least in part to control movements. Further, virtual games and/or movies may be controlled via user input with respect to a 3D map.
The rendering of such 3D-maps is typically accomplished by measuring distances to many points in the 3D space to determine the existence of objects and their respective distances from the vehicle. The rendered 3D-maps may be combined and processed to produce driving decisions by the vehicle. Existing solutions for rendering detailed 3D-maps are based on LiDar (or LADAR) systems. A LiDar system measures distance to an object by illuminating multiple targets (points in the space) with one laser beam or multiple laser beams. Such existing solutions configure the LiDar system to scan the entire environment (scene). This requires a large number of laser measurements to render a single 3D-map.
For example, FIG. 1 shows an image 100 of a scene for which a 3D-map is generated. Some existing solutions implemented by hands-free and autonomous driving technologies measure the distance to each point 110 in the image 100. Thus, a laser beam illuminates each such point to render the 3D-map. In many examples, the LiDar system does not have any prior knowledge of the scene, e.g., a picture of the scene. To this aim, such technologies are based on very complex and expensive equipment. For example, a robotic car made by Google® includes equipment with a LiDar system worth about $70,000. The LiDar system includes a 64-beam laser. Due to the high cost of the hardware for rendering the 3D-maps, mass production of autonomous vehicles is not feasible. It should be noted that only a few points 110 are specifically labeled in FIG. 1 merely for simplicity purposes.
In addition, widespread production of the autonomous vehicle using existing Lidar systems would create hazardous conditions to pedestrians, drivers and/or passengers because of the high number of laser beams that would be transmitted from each vehicle and would likely hit a person in the line of sight. Further, the existing LiDar solutions are configured to transmit laser beams at the highest available energy level. This is performed to measure a point at a maximum range of the Lidar system.
Moreover, the generation of 3D-maps by scanning the entire scene will increase the crosstalk. This is due to the high number of laser beams being transmitted by autonomous vehicles equipped with such scanning systems. As a result, the resulting resolution of 3D-maps generated by scanning the entire scene may be limited.
Further, some existing solutions are configured to transmit pulses of light at the highest available energy for each distance being measured. However, the amount of energy actually needed to accurately determine the distance to an object may vary based on the method utilized to determine distance, the resolution required, the material of the object (e.g., color of the material), the angle of the object relative to the source of light, and the distance to the object. Thus, use of only laser beams having the highest available energy frequently results in using higher energy levels than may be required. This unnecessarily wastes energy and increases the risk of harm to people occupying areas in which laser-based and similar measurements are performed.
It would therefore be advantageous to provide a solution for generating 3D-maps that would overcome the deficiencies of the prior art.